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  <title>Description of filter_gauss_nD</title>
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<h1>filter_gauss_nD
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<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>n-dimensional Gaussian filter.</strong></div>

<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function G = filter_gauss_nD( dims, mu, C, show ) </strong></div>

<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> n-dimensional Gaussian filter. 

 Creates an image of a Gaussian with arbitrary covariance matrix. The point [x,y,z]
 refers to the x-th column and y-th row, at the t-th frame.  So for example mu should
 have the format [col,row,t].

 If mu==[], it is calculated to be the center of the image.  C can be a full nxn
 covariance matrix, or an nx1 vector of variance.  In the latter case C is calculated as
 C=diag(C).  If C=[]; then C=(dims/6).^2, ie it is transformed into a vector of variances
 such that along each dimension the variance is equal to (siz/6)^2.  

 INPUTS
   dims    - n element vector of dimensions of final Gaussian
   mu      - [optional] n element vector specifying the mean or []
   C       - [optional] nxn covariance matrix, nx1 set of variances, or variance, or []
   show    - [optional] figure to use for display (no display if == 0)

 OUTPUTS
   G   - image of the created Gaussian

 EXAMPLE
   % 2D
   sigma=3; G = filter_gauss_nD( 4*[sigma sigma] + 1, [], [sigma sigma].^2, 1 );
   % 3D
   R = rotation_matrix3D( [1,1,0], pi/4 ); 
   C = R'*[10^2 0 0; 0 5^2 0; 0 0 16^2]*R;
   G = filter_gauss_nD( [50,50,50], [25,25,25], C, 1 );

 DATESTAMP
   29-Sep-2005  2:00pm

 See also <a href="filter_gauss_1D.html" class="code" title="function f = filter_gauss_1D( r, sigma, show )">FILTER_GAUSS_1D</a></pre></div>

<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="filter_visualize_1D.html" class="code" title="function filter_visualize_1D( f )">filter_visualize_1D</a>	Used to help visualize the a 1D filter.</li><li><a href="filter_visualize_3D.html" class="code" title="function filter_visualize_3D( F, frac )">filter_visualize_3D</a>	Used to help visualize a 3D filter.</li><li><a href="../images/im.html" class="code" title="function im( I, range );">im</a>	IM [2D] Function for displaying grayscale images.</li><li><a href="../images/montage2.html" class="code" title="function varargout = montage2( IS, showlines, extrainfo, clim, mm, nn, labels )">montage2</a>	[3D] Used to display a stack of T images.</li><li><a href="../matlab/normpdf2.html" class="code" title="function ps = normpdf2(xs,m,C);">normpdf2</a>	Normal prob. density function (pdf) with arbitrary covariance matrix.</li><li><a href="../matlab/plot_gaussellipses.html" class="code" title="function plot_gaussellipses( mus, Cs, d )">plot_gaussellipses</a>	Plots 2D ellipses derived from 2D Gaussians specified by mus & Cs.</li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="../images/imageMLG.html" class="code" title="function varargout = imageMLG( G, symmFlag, show )">imageMLG</a>	Calculates maximum likelihood parameters of gaussian that gave rise to image G.</li><li><a href="../images/mask_gaussians.html" class="code" title="function [masks,keeplocs] = mask_gaussians( siz, M, windowwidth, offset, show )">mask_gaussians</a>	Divides a volume into softly overlapping gaussian windows.</li></ul>
<!-- crossreference -->



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